Prospective Study of Falls and Risk Factors for Falls in Adults With Advanced Cancer
Bibliographic record
Abstract
PURPOSE: Retrospective studies of inpatients with cancer suggest that a cancer diagnosis confers a high risk of falls. In adults with advanced cancer, we aimed to prospectively document the incidence of falls, identify the risk factors, and determine if falls in this population occur predominantly in older patients. PATIENTS AND METHODS: Patients admitted consecutively to community and inpatient palliative care services with metastatic or locoregionally advanced cancer who were mobile without assistance were recruited. Risk-factor assessment was conducted on initial encounter. Patients underwent follow-up via weekly telephone contact for 6 months or until time of fall or death. Relationship between covariates and time to fall was examined using hazard ratios (HRs) derived from univariate and multivariate Cox proportional hazards models. RESULTS: Of 185 participants (52.4% men; mean age 68 ± standard deviation of 12.6 years), 50.3% fell; 35 (53%) of 66 participants age < 65 years and 58 (48.7%) of 119 age ≥ 65 years fell; 61.3% of falls occurred in the community; 42% resulted in injury. Median time to fall was 96 days (95% CI, 64.66 to 127.34). Primary brain tumor or brain metastasis (HR 2.5; P = .002), number of falls in the preceding 3 months (HR, 1.27; P = .005), severity of depression (HR, 1.12; P = .012), benzodiazepine dose (HR, 1.05; P = .004), and cancer-related pain (HR, 1.96; P = .024) were independently associated with time to fall in multivariate analysis. CONCLUSION: Fifty percent of adults with advanced cancer, regardless of age, will experience a fall associated with high risk of physical injury. There is a compelling need to assess the efficacy of assessment and management of modifiable fall risk factors in patients with advanced cancer.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".